In [1]:
#NumPy is a Python Library used to perform wide variety of Mathematical Operations on Arrays.
import numpy as np
#Pandas is a Python Library used to analyze big data and make Conclusions based on Stastical Theories.
import pandas as pd
#matplotlib.pyplot contains functions that make matplotlib work like MATLAB and
#each pyplot fuction makes some changes in the figure.
import matplotlib.pyplot as plt
#plotly.express contains functions that can create Entire Figures.
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import iplot, plot
from plotly.subplots import make_subplots
import seaborn as sns

Importing the Dataset.¶

In [3]:
#Loading the Dataset.
Data=pd.read_csv("C:\\Users\\Admin\\Desktop\\Madhu\\Anaconda-Jupyter\\Spotify 2000-2019 Jupyter Project\\Raw Data.csv")
In [4]:
#To get First 5 Rows of Dataset.
Data.head()
Out[4]:
artist song duration_ms explicit year popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo genre
0 Britney Spears Oops!...I Did It Again 211160 False 2000 77 0.751 0.834 1 -5.444 0 0.0437 0.3000 0.000018 0.3550 0.894 95.053 pop
1 blink-182 All The Small Things 167066 False 1999 79 0.434 0.897 0 -4.918 1 0.0488 0.0103 0.000000 0.6120 0.684 148.726 rock, pop
2 Faith Hill Breathe 250546 False 1999 66 0.529 0.496 7 -9.007 1 0.0290 0.1730 0.000000 0.2510 0.278 136.859 pop, country
3 Bon Jovi It's My Life 224493 False 2000 78 0.551 0.913 0 -4.063 0 0.0466 0.0263 0.000013 0.3470 0.544 119.992 rock, metal
4 *NSYNC Bye Bye Bye 200560 False 2000 65 0.614 0.928 8 -4.806 0 0.0516 0.0408 0.001040 0.0845 0.879 172.656 pop
In [5]:
#To get Last 5 Rows of Dataset.
Data.tail()
Out[5]:
artist song duration_ms explicit year popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo genre
1995 Jonas Brothers Sucker 181026 False 2019 79 0.842 0.734 1 -5.065 0 0.0588 0.0427 0.000000 0.1060 0.952 137.958 pop
1996 Taylor Swift Cruel Summer 178426 False 2019 78 0.552 0.702 9 -5.707 1 0.1570 0.1170 0.000021 0.1050 0.564 169.994 pop
1997 Blanco Brown The Git Up 200593 False 2019 69 0.847 0.678 9 -8.635 1 0.1090 0.0669 0.000000 0.2740 0.811 97.984 hip hop, country
1998 Sam Smith Dancing With A Stranger (with Normani) 171029 False 2019 75 0.741 0.520 8 -7.513 1 0.0656 0.4500 0.000002 0.2220 0.347 102.998 pop
1999 Post Malone Circles 215280 False 2019 85 0.695 0.762 0 -3.497 1 0.0395 0.1920 0.002440 0.0863 0.553 120.042 hip hop
In [6]:
#To get Random 5 Sample Rows from the Dataset
Data.sample(5)
Out[6]:
artist song duration_ms explicit year popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo genre
1605 Desiigner Panda 246761 True 2016 70 0.576 0.766 10 -4.943 0 0.4490 0.02800 0.000002 0.3660 0.236 144.833 hip hop, pop
88 Marc Anthony You Sang To Me 347106 False 1999 56 0.578 0.894 10 -5.420 1 0.0296 0.01030 0.000003 0.2160 0.741 165.980 pop, latin
1018 Edward Maya Stereo Love - Radio Edit 184573 False 2010 66 0.799 0.783 1 -3.896 0 0.0322 0.03460 0.018600 0.0757 0.586 127.041 pop
215 Linkin Park In the End 216880 False 2000 83 0.556 0.864 3 -5.870 0 0.0584 0.00958 0.000000 0.2090 0.400 105.143 rock, metal
697 Chris Brown Yo (Excuse Me Miss) 229040 False 2005 69 0.536 0.612 4 -5.847 1 0.2720 0.11900 0.000000 0.2090 0.570 86.768 hip hop, pop, R&B

Checking the Dataset.¶

In [8]:
#To get Shape and Size of the Data.
Data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2000 entries, 0 to 1999
Data columns (total 18 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   artist            2000 non-null   object 
 1   song              2000 non-null   object 
 2   duration_ms       2000 non-null   int64  
 3   explicit          2000 non-null   bool   
 4   year              2000 non-null   int64  
 5   popularity        2000 non-null   int64  
 6   danceability      2000 non-null   float64
 7   energy            2000 non-null   float64
 8   key               2000 non-null   int64  
 9   loudness          2000 non-null   float64
 10  mode              2000 non-null   int64  
 11  speechiness       2000 non-null   float64
 12  acousticness      2000 non-null   float64
 13  instrumentalness  2000 non-null   float64
 14  liveness          2000 non-null   float64
 15  valence           2000 non-null   float64
 16  tempo             2000 non-null   float64
 17  genre             2000 non-null   object 
dtypes: bool(1), float64(9), int64(5), object(3)
memory usage: 267.7+ KB
In [9]:
#To get the Shape of the Dataset.
Data.shape
Out[9]:
(2000, 18)
In [10]:
#To get all the Information regarding the Dataset.
Data.describe()
Out[10]:
duration_ms year popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo
count 2000.000000 2000.00000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000
mean 228748.124500 2009.49400 59.872500 0.667438 0.720366 5.378000 -5.512434 0.553500 0.103568 0.128955 0.015226 0.181216 0.551690 120.122558
std 39136.569008 5.85996 21.335577 0.140416 0.152745 3.615059 1.933482 0.497254 0.096159 0.173346 0.087771 0.140669 0.220864 26.967112
min 113000.000000 1998.00000 0.000000 0.129000 0.054900 0.000000 -20.514000 0.000000 0.023200 0.000019 0.000000 0.021500 0.038100 60.019000
25% 203580.000000 2004.00000 56.000000 0.581000 0.622000 2.000000 -6.490250 0.000000 0.039600 0.014000 0.000000 0.088100 0.386750 98.985750
50% 223279.500000 2010.00000 65.500000 0.676000 0.736000 6.000000 -5.285000 1.000000 0.059850 0.055700 0.000000 0.124000 0.557500 120.021500
75% 248133.000000 2015.00000 73.000000 0.764000 0.839000 8.000000 -4.167750 1.000000 0.129000 0.176250 0.000068 0.241000 0.730000 134.265500
max 484146.000000 2020.00000 89.000000 0.975000 0.999000 11.000000 -0.276000 1.000000 0.576000 0.976000 0.985000 0.853000 0.973000 210.851000
In [11]:
#To check the Null Values in the Dataset.
Data.isnull().sum()
Out[11]:
artist              0
song                0
duration_ms         0
explicit            0
year                0
popularity          0
danceability        0
energy              0
key                 0
loudness            0
mode                0
speechiness         0
acousticness        0
instrumentalness    0
liveness            0
valence             0
tempo               0
genre               0
dtype: int64

Analysing and Visualizing the Dataset.¶

  1. To know the Number of Artists and their Hit Songs in the Dataset.
In [14]:
#We get to know there are 835 Artists in Total.
Artist=Data['artist'].value_counts()
Artist
Out[14]:
artist
Rihanna           25
Drake             23
Eminem            21
Calvin Harris     20
Britney Spears    19
                  ..
Sidney Samson      1
Cam’ron          1
Elvis Presley      1
Lucenzo            1
Blanco Brown       1
Name: count, Length: 835, dtype: int64
  1. To get the List of Artists based on thier Popularity.
In [16]:
 Popular_Artist=Data.groupby('artist')[['artist','explicit','danceability','popularity','loudness',
                        'energy','speechiness','instrumentalness','acousticness','liveness',
                        'genre']].sum().sort_values('popularity',ascending=False)
Popular_Artist
Out[16]:
artist explicit danceability popularity loudness energy speechiness instrumentalness acousticness liveness genre
artist
Rihanna RihannaRihannaRihannaRihannaRihannaRihannaRiha... 8 16.482 1803 -137.276 16.844 1.9983 0.153460 3.811065 3.7089 hip hop, pop, R&Bhip hop, pop, R&Bhip hop, pop...
Eminem EminemEminemEminemEminemEminemEminemEminemEmin... 20 15.603 1519 -83.568 16.374 4.6724 0.001437 2.212270 4.0052 hip hophip hophip hophip hophip hophip hophip ...
Drake DrakeDrakeDrakeDrakeDrakeDrakeDrakeDrakeDrakeD... 18 16.859 1424 -164.954 13.767 3.7478 0.100460 1.995427 3.8368 hip hop, pop, R&Bhip hop, pop, R&Bhip hop, pop...
Calvin Harris Calvin HarrisCalvin HarrisCalvin HarrisCalvin ... 1 13.719 1356 -77.276 17.429 1.1381 1.041232 1.559682 4.3994 hip hop, pop, Dance/Electronichip hop, pop, Da...
Britney Spears Britney SpearsBritney SpearsBritney SpearsBrit... 1 13.457 1198 -104.603 14.198 1.3786 0.028039 4.452690 2.8750 poppoppoppoppoppoppoppoppoppoppoppoppoppoppopp...
... ... ... ... ... ... ... ... ... ... ... ...
Silentó Silentó 0 0.819 0 -8.522 0.768 0.1340 0.000000 0.234000 0.3340 hip hop
Storm Queen Storm Queen 0 0.832 0 -8.035 0.815 0.0810 0.005530 0.003040 0.2630 Dance/Electronic
AJR AJR 0 0.673 0 -4.518 0.637 0.0429 0.000000 0.137000 0.1840 rock
The Lumineers The Lumineers 0 0.664 0 -6.429 0.576 0.0286 0.000198 0.630000 0.0902 pop, Folk/Acoustic
Kungs Kungs 0 0.790 0 -4.684 0.705 0.0383 0.000048 0.080700 0.2510 pop, Dance/Electronic

835 rows × 11 columns

3)(i) To get the Top 10 Popular Artists.

In [18]:
Popular_Artist.head(10)
Out[18]:
artist explicit danceability popularity loudness energy speechiness instrumentalness acousticness liveness genre
artist
Rihanna RihannaRihannaRihannaRihannaRihannaRihannaRiha... 8 16.482 1803 -137.276 16.844 1.9983 0.153460 3.811065 3.7089 hip hop, pop, R&Bhip hop, pop, R&Bhip hop, pop...
Eminem EminemEminemEminemEminemEminemEminemEminemEmin... 20 15.603 1519 -83.568 16.374 4.6724 0.001437 2.212270 4.0052 hip hophip hophip hophip hophip hophip hophip ...
Drake DrakeDrakeDrakeDrakeDrakeDrakeDrakeDrakeDrakeD... 18 16.859 1424 -164.954 13.767 3.7478 0.100460 1.995427 3.8368 hip hop, pop, R&Bhip hop, pop, R&Bhip hop, pop...
Calvin Harris Calvin HarrisCalvin HarrisCalvin HarrisCalvin ... 1 13.719 1356 -77.276 17.429 1.1381 1.041232 1.559682 4.3994 hip hop, pop, Dance/Electronichip hop, pop, Da...
Britney Spears Britney SpearsBritney SpearsBritney SpearsBrit... 1 13.457 1198 -104.603 14.198 1.3786 0.028039 4.452690 2.8750 poppoppoppoppoppoppoppoppoppoppoppoppoppoppopp...
David Guetta David GuettaDavid GuettaDavid GuettaDavid Guet... 2 11.089 1185 -69.850 14.444 1.3803 0.176915 1.622460 4.0110 hip hop, pop, Dance/Electronichip hop, pop, Da...
Taylor Swift Taylor SwiftTaylor SwiftTaylor SwiftTaylor Swi... 0 10.105 1125 -94.796 11.222 1.2955 0.012130 1.158330 2.1732 poppoppoppoppoppoppoppoppoppoppoppoppoppoppoppop
Chris Brown Chris BrownChris BrownChris BrownChris BrownCh... 6 11.652 1080 -87.028 11.546 1.8561 0.004574 0.760747 2.3684 hip hop, pop, R&Bhip hop, pop, R&Bhip hop, pop...
Kanye West Kanye WestKanye WestKanye WestKanye WestKanye ... 14 10.658 1049 -105.484 12.002 3.7812 1.000080 1.765526 4.3074 hip hophip hophip hophip hophip hophip hophip ...
Katy Perry Katy PerryKaty PerryKaty PerryKaty PerryKaty P... 0 10.233 1033 -73.576 12.462 0.8995 0.000055 0.373918 3.4892 poppoppoppoppoppoppoppoppoppoppoppoppoppoppoppop

3)(ii) To Plot the Bar Graph.

In [20]:
Name=Artist[:10].index
Songs=Artist[:10]
fig=plt.figure(figsize=(15,5))
plt.bar(Name,Songs,width=0.4,color="red")
plt.xticks(rotation=45,ha='right',fontsize=13)
plt.yticks(fontsize=15)
plt.xlabel('Artists',fontsize=20)
plt.ylabel('Number of Songs',fontsize=20)
plt.title('Top 10 Artists with Hit Songs',color='black',fontsize=30)
plt.show()
No description has been provided for this image

4)(i) To get the List of Artists with the Average Popularity of their Songs.

In [22]:
Average_Popularity=Data[['artist','popularity']].groupby('artist').mean().sort_values(by='artist')
Artists=Artist.sort_index()
Average_Popularity['total songs']=Artists.values
Average_Popularity.sort_values(by='total songs',ascending=False,inplace=True)
Average_Popularity.reset_index(inplace=True)
Average_Popularity
Out[22]:
artist popularity total songs
0 Rihanna 72.120000 25
1 Drake 61.913043 23
2 Eminem 72.333333 21
3 Calvin Harris 67.800000 20
4 Britney Spears 63.052632 19
... ... ... ...
830 Khia 59.000000 1
831 Kevin Rudolf 66.000000 1
832 Kevin Lyttle 67.000000 1
833 Kevin Gates 69.000000 1
834 Kungs 0.000000 1

835 rows × 3 columns

4)(ii) To Plot the Scatter Graph of Top 20.

In [24]:
iplot(px.scatter(Average_Popularity[:20],x='artist',y='popularity',size_max=40,size='total songs',
                 color='popularity',title='Top 20 Artists with Average Popularity of their Top Hits',
                labels={'artist':'Artist','popularity':'Popularity'}))

4)(iii) To Plot the Scatter Graph of Top 10.

In [26]:
iplot(px.scatter(Average_Popularity[:10],x='artist',y='popularity',size_max=40,size='total songs',
        color='popularity',title='<b>Top 10 Artists with Average Popularity of their Top Hits</b>',
        labels={'artist':'Artist','popularity':'Popularity'}))

5)(i) To get the List of all the Genres.

In [28]:
Genre=Data['genre'].value_counts()
Genre
Out[28]:
genre
pop                                      428
hip hop, pop                             277
hip hop, pop, R&B                        244
pop, Dance/Electronic                    221
pop, R&B                                 178
hip hop                                  124
hip hop, pop, Dance/Electronic            78
rock                                      58
rock, pop                                 43
Dance/Electronic                          41
rock, metal                               38
pop, latin                                28
pop, rock                                 26
set()                                     22
hip hop, Dance/Electronic                 16
latin                                     15
pop, rock, metal                          14
hip hop, pop, latin                       14
R&B                                       13
pop, rock, Dance/Electronic               13
country                                   10
metal                                      9
hip hop, pop, rock                         9
rock, pop, Dance/Electronic                8
pop, Folk/Acoustic                         8
pop, country                               8
pop, R&B, Dance/Electronic                 6
rock, pop, metal                           4
hip hop, pop, R&B, Dance/Electronic        3
hip hop, R&B                               3
hip hop, pop, R&B, latin                   3
World/Traditional, pop, Folk/Acoustic      2
pop, rock, Folk/Acoustic                   2
pop, easy listening, jazz                  2
World/Traditional, rock                    2
rock, blues, latin                         2
World/Traditional, rock, pop               2
hip hop, rock, pop                         2
rock, blues                                2
Folk/Acoustic, pop                         2
World/Traditional, hip hop                 2
rock, pop, metal, Dance/Electronic         1
rock, classical                            1
country, latin                             1
hip hop, latin, Dance/Electronic           1
rock, Folk/Acoustic, pop                   1
easy listening                             1
Folk/Acoustic, rock, pop                   1
rock, easy listening                       1
pop, R&B, easy listening                   1
World/Traditional, pop                     1
hip hop, pop, country                      1
rock, Dance/Electronic                     1
rock, R&B, Folk/Acoustic, pop              1
World/Traditional, Folk/Acoustic           1
pop, easy listening, Dance/Electronic      1
Folk/Acoustic, rock                        1
rock, Folk/Acoustic, easy listening        1
hip hop, country                           1
Name: count, dtype: int64

5)(ii) To Plot a Histogram.

In [30]:
Fig=px.histogram(Data.groupby('genre',as_index=False).count().sort_values(by='song',ascending=False),
                    x='genre',y='song',color_discrete_sequence=['green'],template='plotly_dark',
                    marginal='box',title='<b>Total no of Songs based on Genres</b>',
                 labels={'sum of song':'Total No of Songs','genre':'Genres'})
Fig.update_layout(title_x=0.5)
#Top 10 can also be plotted by making: .sort_values(by='song',ascending=False).head(10)

5)(iii) To Plot a Bar Graph of Top 10 Genres.

In [32]:
Genre_Name=Genre[:10].index
Number_of_Songs=Genre[:10]
fig=plt.figure(figsize=(10,4))
plt.bar(Genre_Name,Number_of_Songs,width=0.5,color='blue')
plt.xticks(rotation=45,ha='right',fontsize=10)
plt.yticks(fontsize=10)
plt.xlabel("Genres",fontsize=15)
plt.ylabel("No of Songs",fontsize=15)
plt.title("Top 10 Genres that are Hit",color='black',fontsize=20)
plt.show()
No description has been provided for this image

5)(iv) To Plot a Bar Graph of Top 5 Genres

In [34]:
Genre_Name=Genre[:5].index
No_of_Songs=Genre[:5]
fig=plt.figure(figsize=(10,4))
plt.bar(Genre_Name,No_of_Songs,width=0.5,color='green')
plt.xticks(rotation=45,ha='right',fontsize=10)
plt.yticks(fontsize=10)
plt.xlabel("Genre Names",fontsize=15)
plt.ylabel("No of Songs",fontsize=15)
plt.title("Top 5 Genres that are Hit",color='black',fontsize=15)
plt.show()
No description has been provided for this image

6)(i) To get the List of all the Hit Songs realesed in each Year.

In [36]:
Song_Year=Data['year'].value_counts()
Song_Year
Out[36]:
year
2012    115
2017    111
2001    108
2018    107
2010    107
2014    104
2005    104
2011     99
2016     99
2015     99
2003     97
2008     97
2004     96
2006     95
2007     94
2002     90
2019     89
2013     89
2009     84
2000     74
1999     38
2020      3
1998      1
Name: count, dtype: int64

6)(ii) To Plot the Bar Graph.

In [38]:
Song_Year=Data['year'].value_counts().rename_axis('year').reset_index(name='songs')
plt.figure(figsize=(15,6))
plt.xticks(rotation=45,ha='right',fontsize=13)
plt.yticks(fontsize=15)
plt.title("No of songs realesed in each year",fontsize=30)
plt.xlabel('',fontsize=20)
plt.ylabel('',fontsize=20)
Graph=sns.barplot(x="year",y="songs",data=Song_Year,palette="flare")
Graph.bar_label(Graph.containers[0],fontsize=10)
Graph.set(xlabel='Year',ylabel='No of Songs')
plt.show(Graph)
No description has been provided for this image

7)(i) To get a List of Top 25 Popular Songs.

In [40]:
Data.sort_values('popularity',axis=0,ascending=False).head(25)[['popularity','song','artist']]
Out[40]:
popularity song artist
1322 89 Sweater Weather The Neighbourhood
1311 88 Another Love Tom Odell
201 87 Without Me Eminem
1613 86 Wait a Minute! WILLOW
6 86 The Real Slim Shady Eminem
1929 86 lovely (with Khalid) Billie Eilish
1819 86 lovely (with Khalid) Billie Eilish
1512 85 Daddy Issues The Neighbourhood
1709 85 Perfect Ed Sheeran
1999 85 Circles Post Malone
1304 85 Locked out of Heaven Bruno Mars
1227 85 Locked out of Heaven Bruno Mars
209 85 'Till I Collapse Eminem
1514 85 The Nights Avicii
1601 84 One Dance Drake
1712 84 Bored Billie Eilish
1700 84 Shape of You Ed Sheeran
1910 84 Someone You Loved Lewis Capaldi
1701 84 No Lie Sean Paul
1408 84 All of Me John Legend
1511 84 The Hills The Weeknd
1805 84 Lucid Dreams Juice WRLD
1344 84 Do I Wanna Know? Arctic Monkeys
1326 84 Why'd You Only Call Me When You're High? Arctic Monkeys
1905 84 Lost in the Fire (feat. The Weeknd) Gesaffelstein

7)(ii) To Plot the Line Graph.

In [42]:
Figure=px.line(Data.sort_values(by='popularity',ascending=False).head(25),x='song',y='popularity',
               hover_data=['artist'],color_discrete_sequence=['green'],markers=True,
               title='<b> Top 25 Popular Songs in Spotify',
               labels={'popularity':'Popularity','song':'Songs'})
Figure.update_layout(title_x=0.5)
Figure.show()

7)(iii) To get the List of Top 10 Popular Songs.

In [44]:
Data.sort_values('popularity',axis=0,ascending=False).head(10)[['popularity','song','artist']]
Out[44]:
popularity song artist
1322 89 Sweater Weather The Neighbourhood
1311 88 Another Love Tom Odell
201 87 Without Me Eminem
1613 86 Wait a Minute! WILLOW
6 86 The Real Slim Shady Eminem
1929 86 lovely (with Khalid) Billie Eilish
1819 86 lovely (with Khalid) Billie Eilish
1512 85 Daddy Issues The Neighbourhood
1709 85 Perfect Ed Sheeran
1999 85 Circles Post Malone

7)(iv) To get the List of Top 5 Popular Songs.

In [46]:
Data.sort_values('popularity',axis=0,ascending=False).head(5)[['popularity','song','artist']]
Out[46]:
popularity song artist
1322 89 Sweater Weather The Neighbourhood
1311 88 Another Love Tom Odell
201 87 Without Me Eminem
1613 86 Wait a Minute! WILLOW
6 86 The Real Slim Shady Eminem

8)(i) To Convert Duration from ms into min:sec.

In [48]:
#To Define a Function to Convert Duration from ms into min:sec.
def ms_to_minsec(ms):
    sec=ms/1000
    return f"{int(sec//60)}:{int(sec%60)}"
In [49]:
#To Skip the Year 1998 and 1999.
Duration=Data[['duration_ms','year']].groupby('year').mean().reset_index().iloc[2:22]
#To Convert ms into min:sec.
Duration['duration_s']=Duration['duration_ms']/1000
#To Apply ms_to_minsec(ms) Function.
Duration['min:sec']=Duration['duration_ms'].apply(ms_to_minsec)

8)(ii) To Plot the Line Graph of Duration of Songs.

In [51]:
iplot(px.line(Duration,x='year',y='duration_s',color_discrete_sequence=['red'],
              template='plotly_dark',title='<b>Average Song duration over the Years</b>',
              text='min:sec',labels={'year':'Year','duration_s':'Duration'}).update_xaxes(type=
                                            'category').update_traces(textposition='top right'))

We Observe that the Average Duration of Hit Songs kept on Decreasing Year by Year. This Indicates that the People are liking Short Duration Songs.

9)(i) To Find the Longest Hit Song.

In [54]:
Longest_Song=Data.loc[:,['artist','song','duration_ms','year']]
Longest_Song['min:sec']=Longest_Song['duration_ms'].apply(ms_to_minsec)
Longest_Song[Longest_Song.duration_ms==Longest_Song.duration_ms.max()]
Out[54]:
artist song duration_ms year min:sec
1381 Justin Timberlake Mirrors 484146 2013 8:4

9)(ii) To Find the Shortest Hit Song.

In [56]:
Shortest_Song=Data.loc[:,['artist','song','duration_ms','year']]
Shortest_Song['min:sec']=Shortest_Song['duration_ms'].apply(ms_to_minsec)
Shortest_Song[Shortest_Song.duration_ms==Shortest_Song.duration_ms.min()]
Out[56]:
artist song duration_ms year min:sec
1931 Lil Nas X Old Town Road 113000 2019 1:53

10)(i) To Get the Correlation Matrix.

In [58]:
Numeric_data = Data.select_dtypes(include=[np.number])
In [59]:
Correlation_matrix = Numeric_data.corr()
Correlation_matrix
Out[59]:
duration_ms year popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo
duration_ms 1.000000 -0.316534 0.050617 -0.060057 -0.078763 -0.002560 -0.079912 -0.003848 0.066998 0.010923 -0.004208 0.024941 -0.116870 -0.028603
year -0.316534 1.000000 -0.003825 0.033532 -0.108644 0.007380 0.017479 -0.007358 0.001111 0.033809 -0.050265 -0.027037 -0.209365 0.076867
popularity 0.050617 -0.003825 1.000000 -0.003546 -0.014021 0.014823 0.030632 -0.021353 0.021162 0.024619 -0.048059 -0.009856 -0.016142 0.014288
danceability -0.060057 0.033532 -0.003546 1.000000 -0.104038 0.032731 -0.033315 -0.067528 0.145590 -0.065429 0.023207 -0.126413 0.403178 -0.173418
energy -0.078763 -0.108644 -0.014021 -0.104038 1.000000 -0.003446 0.651016 -0.040651 -0.057018 -0.445469 0.037861 0.156761 0.334474 0.153719
key -0.002560 0.007380 0.014823 0.032731 -0.003446 1.000000 -0.007474 -0.153182 0.007147 0.002365 -0.008173 -0.033071 0.036977 -0.001431
loudness -0.079912 0.017479 0.030632 -0.033315 0.651016 -0.007474 1.000000 -0.028133 -0.076388 -0.310039 -0.104925 0.102159 0.232150 0.080709
mode -0.003848 -0.007358 -0.021353 -0.067528 -0.040651 -0.153182 -0.028133 1.000000 -0.000077 0.005744 -0.038613 0.025439 -0.074681 0.048434
speechiness 0.066998 0.001111 0.021162 0.145590 -0.057018 0.007147 -0.076388 -0.000077 1.000000 0.000394 -0.062954 0.061172 0.073605 0.057747
acousticness 0.010923 0.033809 0.024619 -0.065429 -0.445469 0.002365 -0.310039 0.005744 0.000394 1.000000 -0.005214 -0.110043 -0.128128 -0.103660
instrumentalness -0.004208 -0.050265 -0.048059 0.023207 0.037861 -0.008173 -0.104925 -0.038613 -0.062954 -0.005214 1.000000 -0.034897 -0.015192 0.034608
liveness 0.024941 -0.027037 -0.009856 -0.126413 0.156761 -0.033071 0.102159 0.025439 0.061172 -0.110043 -0.034897 1.000000 0.019040 0.028636
valence -0.116870 -0.209365 -0.016142 0.403178 0.334474 0.036977 0.232150 -0.074681 0.073605 -0.128128 -0.015192 0.019040 1.000000 -0.025076
tempo -0.028603 0.076867 0.014288 -0.173418 0.153719 -0.001431 0.080709 0.048434 0.057747 -0.103660 0.034608 0.028636 -0.025076 1.000000

10)(ii) To Plot the Heatmap using Seaborn.

In [61]:
plt.figure(figsize=(12,9))
sns.heatmap(Correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()
No description has been provided for this image
  1. To Plot a Pie Chart to know How many Songs have Explicit Content.
In [63]:
Figure=px.pie(Data.groupby('explicit',as_index=False).count().sort_values(by='song',ascending=False),
              names='explicit',values='song',labels={'song':'Total songs'},hole=0.7,
              color_discrete_sequence=['green','crimson'],template='plotly_dark',
              title='Songs that have Explicit Content')
Figure.update_layout(title_x=0.5)

72.4% of the Songs dont have any Explicit Content.So, most of the Songs are Children Friendly. Less is the Explicit Content, more is the chance of that Song becoming Top Hit.

  1. To Plot the Scatter Graph of Top 10 Explicit Popular Songs.
In [66]:
Top_Explicit_Songs=Data[Data['explicit']==1].nlargest(10,'popularity')
plt.figure(figsize=(10,6))
plt.scatter(Top_Explicit_Songs['song'],Top_Explicit_Songs['popularity'],
            s=Top_Explicit_Songs['popularity']*0.2,#s=shape
            c=Top_Explicit_Songs['popularity'],#c=color
            cmap='tab10')
plt.xlabel('Song',fontsize=15)
plt.ylabel('Popularity',fontsize=15)
plt.title('Top 10 Explicit Popular Songs',fontsize=20)
plt.xticks(rotation=45, ha='right')
plt.colorbar(label='Popularity')
plt.tight_layout()
plt.show()
No description has been provided for this image

13)(i) To get the Year with Most Explicit Songs.

In [68]:
Explicit_Counts_in_Year=Data.groupby('year')['explicit'].sum()
Year_with_Most_Explicit=Explicit_Counts_in_Year.idxmax()
Most_Explicit_Count=Explicit_Counts_in_Year.max()
Year_with_Most_Explicit
Out[68]:
2018

13)(ii) To Plot the Bar Graph.

In [70]:
plt.figure(figsize=(10,6))
plt.bar(Explicit_Counts_in_Year.index,Explicit_Counts_in_Year.values,color='green')
plt.bar(Year_with_Most_Explicit,Most_Explicit_Count,color='orange')
plt.xlabel('Year',fontsize=15)
plt.ylabel('Number of Explicit Songs',fontsize=15)
plt.title('Year with Most Explicit Songs',fontsize=25)
plt.tight_layout()
plt.show()
No description has been provided for this image
  1. To Plot the Histogram Subplots.
In [72]:
Figure=make_subplots(rows=3,cols=3,subplot_titles=('Popularity','Danceability','Energy',
                                                   'Loudness','Speechiness','Acousticness',
                                                   'Liveness','Valence','Tempo'))
Figure.add_trace(go.Histogram(x=Data['popularity'],name='popularity'),row=1,col=1)
Figure.add_trace(go.Histogram(x=Data['danceability'],name='danceability'),row=1,col=2)
Figure.add_trace(go.Histogram(x=Data['energy'],name='energy'),row=1,col=3)
Figure.add_trace(go.Histogram(x=Data['loudness'],name='loudness'),row=2,col=1)
Figure.add_trace(go.Histogram(x=Data['speechiness'],name='speechiness'),row=2,col=2)
Figure.add_trace(go.Histogram(x=Data['acousticness'],name='acousticness'),row=2,col=3)
Figure.add_trace(go.Histogram(x=Data['liveness'],name='liveness'),row=3,col=1)
Figure.add_trace(go.Histogram(x=Data['valence'],name='valence'),row=3,col=2)
Figure.add_trace(go.Histogram(x=Data['tempo'],name='tempo'),row=3,col=3)
Figure.update_layout(height=900,width=900,title_text='<b>Feature Distribution')
Figure.update_layout(template='plotly_dark',title_x=0.5)
In [73]:
New_Data=Data[['duration_ms','year','popularity','danceability','energy','loudness','speechiness',
               'acousticness','instrumentalness','liveness','valence','tempo']]
Data_1={'Heading':['Speechiness','Acousticness','Instrumentalness','Liveness','Valence','Tempo'],
        'Data':[New_Data['speechiness'].mean(),New_Data['acousticness'].mean(),
                New_Data['instrumentalness'].mean(),New_Data['liveness'].mean(),
                New_Data['valence'].mean(),New_Data['tempo'].mean()]}
A=pd.DataFrame(Data_1)
A
Out[73]:
Heading Data
0 Speechiness 0.103568
1 Acousticness 0.128955
2 Instrumentalness 0.015226
3 Liveness 0.181216
4 Valence 0.551690
5 Tempo 120.122558

a) From the above Subplots, we can suggest that : -

  • The Danceability and Energy on Average for the Top Hits are generally HIGH, indicating that these Songs are high on Intensity.
  • The Loudness of the Top Hits are also HIGH compared to the rest of the Songs.

b) From the above Analysis, we can suggest that : -

  • The Speechiness of the Top Hits are quite LOW.
  • The Acousticness of the Tracks are also LOW, suggesting that Songs which tend to become Top Hits are HIGH in Energy and Tempo.
  • The Instrumentalness of the Songs are also LOW, which gives a pretty clear idea as to how Music taste has evolved amongst the Listeners.
  • The late 20th century was dominated by the Bands and as the Years progressed, we got to witness a decline in the number of Bands being popular and thus Instrumentalness also declined over the years except few of them .
  • The Songs which became Top Hits, score LOW on Liveness indicating that the Songs are mostly Recorded.
  • Top Hits on Average, score just above 0.5 on Valence, indicating that while Most of the Top Hits have a Positive mood associated with them, some have quite an equal mixture of both Positive and Sad sounding.
  • The Tempo for Most of the Top Hits on Average is 120bpm, suggesting that the Songs are Peppy and Fast sounding.

Questions to be Answered.¶

  1. To get the Average Popularity of the Songs released in each Year.
In [87]:
#Grouping the Data by Year.
Grouped_by_Year=Data.groupby('year')
#Calculate the Mean of Popularity within Each Group.
Popularity_by_Year=Grouped_by_Year['popularity'].mean()
#Display the Average Popularity for Each Year.
print(Popularity_by_Year)

year
1998    49.000000
1999    64.921053
2000    58.554054
2001    59.407407
2002    62.400000
2003    58.793814
2004    58.427083
2005    61.759615
2006    57.389474
2007    59.925532
2008    63.721649
2009    61.535714
2010    60.747664
2011    58.565657
2012    60.556522
2013    54.786517
2014    59.384615
2015    57.747475
2016    54.171717
2017    53.882883
2018    70.850467
2019    63.011236
2020    43.333333
Name: popularity, dtype: float64
  1. To find the Most Popular Genre for Songs with the Duration Longer than 5 Minutes.
In [89]:
#Filter the Data for Songs with Duration more than 5 Minutes.
Filtered_Data=Data[Data['duration_ms']>300000]
#Group the Filtered Data by Genre.
Grouped_by_Genre=Filtered_Data.groupby('genre')
#Calculate the Mean Popularity within each Genre.
Popularity_by_Genre=Grouped_by_Genre['popularity'].mean()
#Identify the Genre with the Highest Average Popularity.
Most_Popular_Genre=Popularity_by_Genre.idxmax()
print("The Most Popular Genre for Songs with Duration more than 5 minutes is:",Most_Popular_Genre)

The Most Popular Genre for Songs with Duration more than 5 minutes is: hip hop, Dance/Electronic
  1. To find the Artist with Highest Average Danceability and their Most Popular Song.
In [91]:
#Group the Data by Artists.
Grouped_by_Artist=Data.groupby('artist')
#Calculate the Mean Danceability within each Artist Group.
Danceability_by_Artist=Grouped_by_Artist['danceability'].mean()
#Identify the Artist with the Highest Average Danceability.
Artist_with_Highest_Danceability=Danceability_by_Artist.idxmax()
#Filter the Data for Songs by the Artist.
Filtered_by_Artist=Data[Data['artist']==Artist_with_Highest_Danceability]
#Finding the Song with the Highest Popularity for that Artist.
Most_Popular_Song=Filtered_by_Artist.loc[Filtered_by_Artist['popularity'].idxmax(),'song']

print("The Artist with the Highest Average Danceability is:",Artist_with_Highest_Danceability)
print("Their Most Popular Song is:",Most_Popular_Song)

The Artist with the Highest Average Danceability is: Lil Baby
Their Most Popular Song is: Yes Indeed

Conclusions.¶

  1. RIHANNA is the Top Artist with 25 Hit Songs.
  1. POP seems to be the Most Popular type of Genre with 428 Songs of the Top Hits Spotify Songs since 2000-2019.
  1. In the Year 2012, we got the Most number of Top Hits with 115 Songs.
  1. SWEATER WEATHER by The Neighbourhood is the Most Popular Song.
  1. Average Duration of the Songs kept on decreasing year by year.
  1. Less the Explicit Content, more is the Chances of that Song to become Top Hit.
  1. Most of the Hit Songs scored LOW on Speechiness, Liveliness, Instrumentalness and Acousticness which means they are Soothing and feels Relaxed.